{"id":"W4413802745","doi":"10.24908/iqurcp19061","title":"Engineering Hyperthermostable Nylonase, TvgC, to Improve Catalytic Efficiency for Degradation","year":2025,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Enzyme Catalysis and Immobilization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Degradation (telecommunications); Saturated mutagenesis; Nylon 6; Catalysis; Chemistry; Mutagenesis; Inert; Catalytic efficiency; Materials science; Chemical engineering; Biochemical engineering; Combinatorial chemistry; Computer science; Mutant; Biochemistry; Polymer; Organic chemistry; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002669692,0.0002956128,0.0002117165,0.0001648657,0.0001110243,0.0006405752,0.0002401469,0.0003992064,0.0007066402],"category_scores_gemma":[0.0002859336,0.0001800786,0.0002746743,0.0002375782,0.0001678804,0.0003274032,0.0002587684,0.0004661528,0.0003760158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000477781,"about_ca_system_score_gemma":0.0003257108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001733195,"about_ca_topic_score_gemma":0.003297945,"domain_scores_codex":[0.9998291,0.00002291524,0.00001272345,0.00003400261,0.00006655807,0.00003479097],"domain_scores_gemma":[0.9999223,0.00001589832,0.00002016883,0.000011518,0.00001758777,0.0000125698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009610543,0.00001687478,0.0001313153,0.00001428641,0.000002265037,0.000016538,0.00001184524,0.0001920077,0.9968281,0.0001480176,0.00004443578,0.002584585],"study_design_scores_gemma":[0.000006435275,0.00008995244,0.0008351383,0.000003904223,0.000008431015,0.00006060803,0.00002208769,0.00235811,0.9922003,0.00005595974,0.004353335,0.000005769803],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9559075,0.001324741,0.03777135,0.0004559011,0.00006596374,0.00007722282,0.0001307637,0.0003150257,0.003951546],"genre_scores_gemma":[0.9492502,0.002179316,0.04034694,0.0001252292,0.000009261094,0.00003508807,0.0002738251,0.0001032892,0.007676986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001733195,"threshold_uncertainty_score":0.003466547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03724059341067929,"score_gpt":0.3350803672471686,"score_spread":0.2978397738364893,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}